Development of a novel nomogram for the prediction of surgical site infection risk after loop ileostomy closure.

Abstract

Background: A postoperative surgical site infection (SSI) is a prevalent complication after loop ileostomy closure. There are few studies on the risk factors and the development of predictive models for postoperative SSIs. The aim of this study was to develop and validate a nomogram model capable of accurately predicting the occurrence of postoperative SSIs.

Methods: This retrospective analysis examined the clinical data of 369 patients who underwent loop ileostomy closure at a local hospital from January 2015 to March 2022. A logistic regression model was used to identify the potential risk factors for a postoperative SSI after loop ileostomy closure. A nomogram was established using independent risk factors, and the prediction performance of the model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC).

Results: Forty-eight (13.0%) developed postoperative SSIs after loop ileostomy closure. Multivariate logistic regression analysis revealed that a body mass index (BMI) > 25 kg/m², diabetes, linear skin closure (LSC), and a prolonged operative time were independent risk factors for SSIs, whereas the presence of a subcutaneous drainage tube was identified as an independent protective factor. The nomogram models constructed using these variables achieved AUCs of 0.833 and 0.823 on the training set and validation set, respectively. The calibration curves demonstrated excellent consistency.

Conclusion: The nomogram developed using clinical data from patients who underwent loop ileostomy closure demonstrates a robust predictive capability, offering valuable guidance to clinicians in assessing the risk of postoperative SSIs.